通过子图采样和多头注意力融合时间和结构信息,以获得信息级联预测
Jie Wang1, Jinze Li2, Chun Liu1
1School of Management, Capital Normal University, Beijing, 100089, China.
Scientific reports
|February 25, 2025
概括
这项研究介绍了CasSubTS,这是一个用于预测社交网络信息布的新模型. CasSubTS有效地捕捉动态变化,并整合时间和结构数据以提高预测准确度.
科学领域:
- 计算机科学 计算机科学
- 网络科学 网络科学
- 数据科学数据科学数据科学
背景情况:
- 在线社交网络产生了大量的信息级联.
- 现有的深度学习模型与布的动态性质和时空特征作斗争.
- 准确的级联预测对于研究和工业应用至关重要.
研究的目的:
- 开发一种基于子图的新型模型,CasSubTS,用于增强信息级联预测.
- 解决捕捉动态变化和整合时间和结构信息的局限性.
- 提高信息级联预测方法的准确性和稳定性.
主要方法:
- CasSubTS采样时间步骤子图以捕捉动态节点变化.
- 它整合了内度和外度,以增强节点表示,并使用多头图注意力网络进行多尺度结构学习.
- 时间信息是使用注意力机制汇总的,并且道注意力优先考虑时空特征融合,然后通过多层感知器进行增量预测.
主要成果:
- 与其他六种模型相比,CasSubTS在两个真实世界和一个合成数据集上表现出更高的性能.
- 该模型有效地捕捉了动态级联演变,并整合了各种功能.
- 实验结果验证了拟议的基于子图的方法和注意力机制的有效性.
结论:
- CasSubTS通过有效建模动态的时空空间依赖关系,在信息级联预测方面取得了重大进展.
- 分图采样,多头注意力和通道注意力的集成提高了预测准确性.
- 该模型为理解和预测在线社交网络中的信息传播提供了一个强大的框架.
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